Generate vector embeddings with model endpoint management

Before you begin

Make sure that you have registered your model endpoint with Model endpoint management. For more information, see Register a model endpoint with model endpoint management

Generate embeddings

Use the google_ml.embedding() SQL function to call the registered model endpoint with the text embedding model type to generate embeddings.

To call the model and generate embeddings, use the following SQL query:

  SELECT 
  
 google_ml 
 . 
 embedding 
 ( 
  
 model_id 
  
 = 
>  
 ' MODEL_ID 
' 
 , 
  
 content 
  
 = 
>  
 ' CONTENT 
' 
 ); 
 

Replace the following:

• MODEL_ID : the model ID you defined when registering the model endpoint.
• CONTENT : the text to translate into a vector embedding.

Examples

Some examples for generating embeddings using registered model endpoint are listed in this section.

Text embedding models with built-in support

To generate embeddings for a registered text-embedding-005 model endpoint, run the following statement:

   
 SELECT 
  
 google_ml 
 . 
 embedding 
 ( 
  
 model_id 
  
 = 
>  
 'text-embedding-005' 
 , 
  
 content 
  
 = 
>  
 'AlloyDB is a managed, cloud-hosted SQL database service' 
 ); 
 

To generate embeddings for a registered text-embedding-ada-002 model endpoint by OpenAI, run the following statement:

   
 SELECT 
  
 google_ml 
 . 
 embedding 
 ( 
  
 model_id 
  
 = 
>  
 'text-embedding-ada-002' 
 , 
  
 content 
  
 = 
>  
 'e-mail spam' 
 ); 
 

To generate embeddings for a registered text-embedding-3-small or text-embedding-3-large model endpoints by OpenAI, run the following statement:

   
 SELECT 
  
 google_ml 
 . 
 embedding 
 ( 
  
 model_id 
  
 = 
>  
 'text-embedding-3-small' 
 , 
  
 content 
  
 = 
>  
 'Vector embeddings in AI' 
 );